🧠 Hybrid Search

HyperspaceDB combines Hyperbolic Vector Search with Lexical (Keyword) Search to provide the best of both worlds.

This is powered by Reciprocal Rank Fusion (RRF), which normalizes scores from both engines and merges them.

Conceptual Flow

1. Vector Search: Finds semantically similar items (e.g. "smartphone" finds "iPhone").
2. Keyword Search: Finds exact token matches in metadata (e.g. "iphone" finds items with "iphone" in title).
3. RRF Fusion: Score = 1/(k + rank_vec) + 1/(k + rank_lex).

API Usage

Python

results = client.search(
    vector=query_vector,
    top_k=10,
    hybrid_query="apple macbook",  # Lexical query
    hybrid_alpha=0.5               # Balance factor (default 60.0 in RRF usually, but exposed as alpha here)
)

Rust

#![allow(unused)]
fn main() {
let results = client.search_advanced(
    query_vector,
    10,
    vec![], 
    Some(("apple macbook".to_string(), 0.5))
).await?;
}

Tokenization

Currently, all string metadata values are automatically tokenized (split by whitespace, lowercase, alphanumeric) and indexed in an inverted index.